Voice assistants have become ubiquitous, yet they remain vulnerable to network traffic fingerprinting attacks that can expose sensitive user information. Existing defenses either impose high overheads or fail against advanced attacks. This paper addresses these issues by introducing and evaluating PriVA-C, a fingerprinting defense mechanism tailored specifically for voice assistants. Unlike prior approaches that treat voice assistant traffic as generic web traffic, we analyze its unique characteristics to design a more effective defense. Our approach prioritizes limiting information leakage rather than targeting specific attack vectors, achieving a significant reduction in attacker accuracy from 89% to 13%. We also propose a more practically deployable version of our defense, which protects only traffic directed to the primary voice assistant domain, reducing attacker accuracy to 19%. We implement a functional prototype using the Alexa SDK, conduct user testing, and assess its performance using real network traffic. Our results demonstrate that our proposed defense effectively mitigates fingerprinting attacks while maintaining low overhead and preserving the user experience.

Online ads provide essential revenue for millions of websites but often disrupt user experience.To address this, browser extensions emerged to block intrusive ads, prompting the creation of the Acceptable Ads Standards to balance user choice and monetization.The Acceptable Ads Standards, initiated by the Acceptable Ads Committee, seek a balance between user experience and ad effectiveness, allowing certain non-intrusive ads defined by size, placement, and type limitations.This paper analyzes the compliance of digital advertisements with the Acceptable Ads standards by examining 10,000 popular domains intersecting Tranco's top 100K and the Acceptable Ads exception list.Our findings reveal that nearly 10% of these sites display non-compliant ads on landing pages, exposing design flaws in the exception list that allow publishers to bypass size and format restrictions.We propose enhancements to the exception list to better uphold user experience and ad integrity.

Artificial Intelligence (AI) is becoming increasingly indispensable across diverse domains as technology rapidly advances. As traditional energy sources dwindle, there's a noticeable pivot towards renewable energy sources (RES). However, to effectively meet energy demands, integrating these RES into smart grids to bolster efficiency is imperative. Despite the transition, ongoing technical challenges persist, specifically in accurately predicting and optimizing smart grid parameters. To tackle these hurdles and enhance smart grid efficiency, various AI techniques are being harnessed. This study leverages real-time energy generation data (MWh) from solar and wind plants over a year, dependent on parameters such as POA and wind speed, respectively. Prediction outcomes are derived using three machine learning (ML) models (XGBoost, CatBoost, and LightGBM) and three deep learning (DL) models (LSTM, BiLSTM, and GRU). From these individual models, two hybrid ML and DL models are developed, yielding promising results. Subsequently, these outcomes are further refined through a parallel fusion approach (PFA), resulting in heightened accuracy and reliability. The implementation of this technique notably reduces error rates to 15.05% for hybrid ML, 19.18% for hybrid DL, and 8.1432% for PFA. This methodology holds substantial potential for future research endeavors, supplementing existing AI models for enhanced efficiency.

Online trackers are invasive as they track our digital footprints, many of which are sensitive in nature, and when aggregated over time, they can help infer intricate details about our lifestyles and habits. Although much research has been conducted to understand the effectiveness of existing countermeasures for the desktop platform, little is known about how mobile browsers have evolved to handle online trackers. With mobile devices now generating more web traffic than their desktop counterparts, we fill this research gap through a large-scale comparative analysis of mobile web browsers. We crawl 10K valid websites from the Tranco list on real mobile devices. Our data collection process covers both popular generic browsers (e.g., Chrome, Firefox, and Safari) as well as privacy-focused browsers (e.g., Brave, Duck Duck Go, and Firefox-Focus). We use dynamic analysis of runtime execution traces and static analysis of source codes to highlight the tracking behavior of invasive fingerprinters. We also find evidence of tailored content being served to different browsers. In particular, we note that Firefox Focus sees altered script code, whereas Brave and Duck Duck Go have highly similar content. To test the privacy protection of browsers, we measure the responses of each browser in blocking trackers and advertisers and note the strengths and weaknesses of privacy browsers. To establish ground truth, we use well-known block lists, including EasyList, EasyPrivacy, Disconnect and WhoTracksMe and find that Brave generally blocks the highest number of content that should be blocked as per these lists. Focus performs better against social trackers, and Duck Duck Go restricts third-party trackers that perform email-based tracking.

The wide spread of vehicular cameras has raised broad privacy concerns. Ubiquitous vehicular cameras capture bystanders like people or cars nearby without their awareness. To address privacy concerns, most existing works either blur out direct identifiers such as vehicle license plates and human faces, or obfuscate whole video frames. However, the former solution is vulnerable to re-identification attacks based on general features, and the latter severely impacts utility of the transformed videos. In this paper, we propose an INStance-level PrIvacy-pREserving (INSPIRE) video transformation framework for vehicular camera videos. INSPIRE leverages deep neural network models to detect and replace sensitive object instances in vehicular videos with their non-existent counterparts. We design INSPIRE as a modular framework to enable flexible customization of protected instance categories and their protection modules. An implementation of INSPIRE focused on protecting people and cars is described, which we tested on six re-identification datasets and three real-world vehicular video datasets to evaluate its privacy protection and utility preservation capability. Results show that INSPIRE can thwart 97% of re-identification attacks for people and cars while maintaining a 0.75 object detection mean average precision on transformed instances. We also demonstrate experimentally that INSPIRE is robust against model inversion attacks. Compared to solutions that provide comparable privacy protection, INSPIRE achieves relatively 1.76 times higher counting accuracy and 31.61% higher object detection mean average precision.

Targeted advertisement is prevalent on the Web. Many privacy-enhancing tools have been developed to thwart targeted advertisement. Adblock Plus is one such popular tool, used by millions of users on a daily basis, to block unwanted ads and trackers. Adblock Plus uses EasyList and EasyPrivacy, the most prominent and widely used open-source filters, to block unwanted web contents. However, Adblock Plus, by default, also enables an exception list to unblock web requests that comply with specific guidelines defined by the Acceptable Ads Committee. Any publisher can enroll into the Acceptable Ads initiative to request the unblocking of web contents. Adblock Plus in return charges a licensing fee from large entities, who gain a significant amount of ad impressions per month due to participation in the Acceptable Ads initiative. However, the privacy implications of the default inclusion of the exception list has not been well studied, especially as it can unblock not only ads, but also trackers (e.g., unblocking contents otherwise blocked by EasyPrivacy).